Biomechanics of Manual Wheelchair Propulsion in Elderly
Bibliographic record
Abstract
OBJECTIVE: To investigate the effects of the system tilt and back recline angles on the biomechanics of wheelchair propulsion for a group of older, disabled patients. It was hypothesized that increasing both the system tilt and backrest recline angles would have a positive effect on the biomechanical efficiency of manual wheelchair propulsion. DESIGN: Three kinetic variables were estimated during a 10-m, steady-state propulsion between 0.96 m/sec and 1.01 m/sec. The fraction of the mechanical effective force is defined by the ratio between the tangential and the total force applied to the pushrim: It expresses the directionality of force application. The mechanical use is defined as the ratio between the total force generated during wheelchair propulsion and that generated during maximal isometric contraction. The biomechanical efficiency is defined as the product of mechanical effective force and the mechanical use. RESULTS: On average, the fraction of the mechanical effective force was found to be low when compared with other studies. Tilting the system by 10 degrees and reclining the back by 10 degrees increase significantly the biomechanical efficiency of the subject by 10%. The biomechanical efficiency variable was more sensitive to the system tilt than to the back recline adjustment. CONCLUSIONS: The results of this study confirm the hypothesis that system tilt angle but not back recline significantly affects biomechanical efficiency. The findings of this study will help in designing and adjusting a wheelchair intended for self-propelled, older people.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".